Using TFRecords with keras

Change:

"label": tf.FixedLenSequenceFeature([1]...

into:

"label": tf.FixedLenSequenceFeature([]...

This is unfortunately not explained in the documentation on the website, but some explanation can be found in the docstring of FixedLenSequenceFeature on github. Basically, if your data consists of a single dimension (+ a batch dimension), you don't need to specify it.

You have forget to this line from the example:

parsed_features = tf.parse_single_example(proto, f)

Add it to _parse_function.

Also, you can return just the dataset object. Keras supports iterators as well as instances of the tf.data.Dataset. Also, it looks a bit weird to shuffle and repeat first, and then to parse tfexamples. Here is an example code that works for me:

def dataset(filenames, batch_size, img_height, img_width, is_training=False):
    decoder = TfExampleDecoder()

    def preprocess(image, boxes, classes):
            image = preprocess_image(image, resize_height=img_height, resize_width=img_width)
        return image, groundtruth

    ds = tf.data.TFRecordDataset(filenames)
    ds = ds.map(decoder.decode, num_parallel_calls=8)
    if is_training:
        ds = ds.shuffle(1000 + 3 * batch_size)
    ds = ds.apply(tf.contrib.data.map_and_batch(map_func=preprocess, batch_size=batch_size, num_parallel_calls=8))
    ds = ds.repeat()
    ds = ds.prefetch(buffer_size=batch_size)
    return ds


train_dataset = dataset(args.train_data, args.batch_size,
                        args.img_height, args.img_width,
                        is_training=True)


model.fit(train_dataset,
          steps_per_epoch=args.steps_per_epoch,
          epochs=args.max_epochs,
          callbacks=callbacks,
          initial_epoch=0)

It seems like an issue with your data or preprocessing pipeline, rather than with Keras. Try to inspect what you are getting out of the dataset with a debugging code like:

ds = dataset(args.data, args.img_height, args.img_width, is_training=True)

image_t, classes_t = ds.make_one_shot_iterator().get_next()

with tf.Session() as sess:
    while True:
        image, classes = sess.run([image_t, classes_t])
        # Do something with the data: display, log etc.